Titlebook: Clinical Epidemiology and Biostatistics; A Primer for Clinica Michael S. Kramer Book 1988 Springer-Verlag Berlin Heidelberg 1988 biostatist

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Analytic Bias target population, based on data in a study sample from that target population. As discussed in Chapter 4, both exposure and outcome can be measured on either a continuous or a categorical scale, and the measure of their association usually takes one of two forms: (a) a difference in outcome means

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Observational Cohort Studies studies can be either experimental (exposure assigned by the investigator) or observational (exposure arises naturally, is selected by the subject, or is prescribed by the subject’s clinician). Experimental cohort studies, which are usually called ., have achieved such widespread importance. that w

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Clinical Trialsort studies in some detail in the last chapter, we shall now focus our attention on experimental cohort studies, or clinical trials. In clinical trials, exposure is assigned by the study investigators, rather than occurring naturally or being selected by the subject or his clinician. Consequently, e

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Cross-Sectional Studiesontrol studies, subjects are investigated in a backward direction from outcome to exposure; inference is from effect to cause. In cross-sectional studies, the exposure and outcome are both determined at the same point, or cross section, in time [1]. (Hence, another name for this design is ..) Cross-

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Descriptive Statistics and Data Display. Suppose we wished to describe the variable “age” (in completed years) in 250 patients who underwent cholecystectomy (gallbladder removal) at City Hospital during a 6-month period. Merely listing the 250 patients with their corresponding ages would convey very little useful information, because the

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Hypothesis Testing and , Valuesy exist between exposure and outcome in the target population. It can usually be posed in the form of a statement or question. Consider the example from Chapter 6 of cigarette smoking as a risk factor for myocardial infarction (MI), or heart attack. The research hypothesis might be expressed in term

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Statistical Inference for Continuous Variablesined the mean (.) of that sample, replaced the same subjects back into the source population, then chose another random sample of the same size ., and repeated this process over and over again. What distribution would the repeated sample .’s have? It turns out that if . is large enough, then the .’s

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Statistical Inference for Categorical Variabless well as exposure variables. In the last chapter, we focused our attention on the comparison of two or more means, usually representing the mean outcomes in two or more exposure groups. In such cases, exposure is categorical (e. g., medical vs surgical treatment for renovascular hypertension), and